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Safieddin Ardebili, Seyed Mohamamd, Veza, Ibham, Deniz Karaoglan, Aslan, Ileri, Erol, Kiani Deh Kiani, Mostafa, Şahin, Fatih, Rabeti, Masoud. (1404). Optimizing a low-temperature combustion engine run with various compression ratios using the modified social group technique. , 1(1), 11-20. doi: 10.22069/bere.2025.22943.1002
Seyed Mohamamd Safieddin Ardebili; Ibham Veza; Aslan Deniz Karaoglan; Erol Ileri; Mostafa Kiani Deh Kiani; Fatih Şahin; Masoud Rabeti. "Optimizing a low-temperature combustion engine run with various compression ratios using the modified social group technique". , 1, 1, 1404, 11-20. doi: 10.22069/bere.2025.22943.1002
Safieddin Ardebili, Seyed Mohamamd, Veza, Ibham, Deniz Karaoglan, Aslan, Ileri, Erol, Kiani Deh Kiani, Mostafa, Şahin, Fatih, Rabeti, Masoud. (1404). 'Optimizing a low-temperature combustion engine run with various compression ratios using the modified social group technique', , 1(1), pp. 11-20. doi: 10.22069/bere.2025.22943.1002
Safieddin Ardebili, Seyed Mohamamd, Veza, Ibham, Deniz Karaoglan, Aslan, Ileri, Erol, Kiani Deh Kiani, Mostafa, Şahin, Fatih, Rabeti, Masoud. Optimizing a low-temperature combustion engine run with various compression ratios using the modified social group technique. , 1404; 1(1): 11-20. doi: 10.22069/bere.2025.22943.1002

Optimizing a low-temperature combustion engine run with various compression ratios using the modified social group technique

Biosystems Engineering and Renewable Energies
دوره 1، شماره 1، فروردین 2025، صفحه 11-20    اصل مقاله (1.19 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22069/bere.2025.22943.1002
نویسندگان
Seyed Mohamamd Safieddin Ardebili* 1؛ Ibham Veza2؛ Aslan Deniz Karaoglan3؛ Erol Ileri4؛ Mostafa Kiani Deh Kiani1؛ Fatih Şahin5؛ Masoud Rabeti6
1Department of Biosystems Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2Department of Mechanical Engineering, Faculty of Engineering, Universitas Bung Karno, Jakarta Pusat, Indonesia
3Balikesir University, Department of Industrial Engineering, 10145 Balikesir, Turkey
4National Defense University, Army NCO Vocational HE School, Department of Automotive Sciences, 10110 Balıkesir, Turkey
5Automotive Engineering Departments, Faculty of Technology, Gazi University, Ankara, Turkey
6Department of Mechanical Engineering, Faculty of Engineering, Sousangerd Branch, Islamic Azad University, Sousangerd, Iran
چکیده
This low-temperature combustion study modified a single-cylinder gasoline engine into an HCCI engine. For the HCCI experiments, four different compression ratios were used. The intake air temperatures varied between 313 and 373 K, while the engine speed changed from 800 to 1800 rpm. Three fuel blends were used. The RON60 indicates 60% iso-octane and 40% n-heptane. A modified social group optimization algorithm was used for HCCI optimization purposes. Regression modeling was first employed to calculate the mathematical relations between the factors (i.e., compression ratio, research octane number, intake air temperature, engine speed, and lambda) and the responses (effective torque, IMEP, indicated thermal efficiency, specific fuel consumption, COV IMEP, and HC). For the HCCI performance tests, the regression models fit the given observations well with a low prediction error. The calculated R2 obtained from this study shows that the compression ratio (X1), RON (X2), intake air temperature (X3), engine speed (X4), and lambda (X5) are sufficient to model the responses (effective torque, IMEP, indicated thermal efficiency, specific fuel consumption, COV IMEP, and HC). Then, the MSGO is run via these mathematical models to determine the parameters with optimal optimization values. In the verification phase, 13 additional experimental runs that were not used in the mathematical modeling phase were used. It was found that the regression models fit the observed values well with a low PE (%). The algorithm suggested the best value for studied parameters as X1 = 11.47, X2 = 60, X3 = 313 K, X4 = 800 rpm, and X5 = 1.45. The verification shows satisfying results with a high accuracy. The optimized factor levels indicate that the effective torque, IMEP, and indicated thermal efficiency were maximized while the other responses were minimized. Therefore, the findings signify the potential of the algorithm for HCCI optimization.

تازه های تحقیق
  • A modified social group optimization or HCCI optimization purposes was used for numerical modeling
  • Improved engine performance and reduced exhaust emissions recorded
  • Best engine performance condition proposed
کلیدواژه‌ها
HCCI optimization؛ HCCI performance؛ Regression modeling optimization؛ Social group optimization
مراجع

Abdelmalek, Z., Alamian, R., Safdari Shadloo, M., Maleki, A., & Karimipour, A. (2021). Numerical study on the performance of a homogeneous charge compression ignition engine fueled with different blends of biodiesel. Journal of Thermal Analysis and Calorimetry, 143(3), 2695-2705.

AlShabi, M., Ghenai, C., Bettayeb, M., Ahmad, F. F., & El Haj Assad, M. (2021). Multi-group grey wolf optimizer (MG-GWO) for estimating photovoltaic solar cell model. Journal of Thermal Analysis and Calorimetry, 144(5), 1655-1670.

Atmanlı, A., Yüksel, B., Ileri, E., & Karaoglan, A. D. (2015). Response surface methodology based optimization of diesel–n-butanol–cotton oil ternary blend ratios to improve engine performance and exhaust emission characteristics. Energy Conversion and Management, 90, 383-394.

Bhadoria, A., & Marwaha, S. (2020). Moth flame optimizer-based solution approach for unit commitment and generation scheduling problem of electric power system. Journal of Computational Design and Engineering, 7(5), 668-683.

Calam, A., Solmaz, H., Yılmaz, E., & İçingür, Y. (2019). Investigation of effect of compression ratio on combustion and exhaust emissions in A HCCI engine. Energy, 168, 1208-1216.

Deh Kiani, M. K., Ghobadian, B., Tavakoli, T., Nikbakht, A. M., & Najafi, G. (2010). Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends. Energy, 35(1), 65-69.

Eiben, A. E., & Schippers, C. A. (1998). On evolutionary exploration and exploitation. Fundamenta Informaticae, 35(1-4), 35-50.

Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.

Ileri, E., Karaoglan, A. D., & Akpinar, S. (2020). Optimizing cetane improver concentration in biodiesel-diesel blend via grey wolf optimizer algorithm. Fuel, 273, 117784.

Ileri, E., Karaoglan, A. D., & Atmanli, A. (2013). Response surface methodology based prediction of engine performance and exhaust emissions of a diesel engine fuelled with canola oil methyl ester. Journal of Renewable and Sustainable Energy, 5(3), 033132.

Karaoglan, A. D. (2021). Optimizing Plastic Extrusion Process via Grey Wolf Optimizer Algorithm and Regression Analysis. Journal of Scientific and Industrial Research, 80(01), 34-41.

Karaoglan, A. D., & Baydeniz, B. (2021). Optimizing Plastic Injection Process Using Whale Optimization Algorithm in Automotive Lighting Parts Manufacturing. Journal of Scientific and Industrial Research, 80(04), 360-368.

Kocakulak, T., Babagiray, M., Nacak, Ç., Safieddin Ardebili, S. M., Calam, A., & Solmaz, H. (2022). Multi objective optimization of HCCI combustion fuelled with fusel oil and n-heptane blends. Renewable Energy, 182, 827-841.

Kocakulak, T., Halis, S., Ardebili, S. M. S., Babagiray, M., Haşimoğlu, C., Rabeti, M., & Calam, A. (2023). Predictive modelling and optimization of performance and emissions of an auto-ignited heavy naphtha/n-heptane fueled HCCI engine using RSM. Fuel, 333, 126519.

Leo, G. M. L., Sekar, S., & Arivazhagan, S. (2020). Experimental investigation and ANN modelling of the effects of diesel/gasoline premixing in a waste cooking oil-fuelled HCCI-DI engine. Journal of Thermal Analysis and Calorimetry, 141(6), 2311-2324.

Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems, 96, 120-133.

Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.

Moghdani, R., & Salimifard, K. (2018). Volleyball premier league algorithm. Applied Soft Computing, 64, 161-185.

Montgomery, D. C. (2017). Design and analysis of experiments. John wiley & sons.

Naik, A. (2021). Modified Social Group Optimization Algorithm. MATLAB Central File Exchange. Retrieved 21 September from https://www.mathworks.com/matlabcentral/fileexchange/78272-modified-social-group-optimization-algorithm

Naik, A., Satapathy, S. C., & Abraham, A. (2020). Modified Social Group Optimization—a meta-heuristic algorithm to solve short-term hydrothermal scheduling. Applied Soft Computing, 95, 106524.

Nematollahi, A. F., Rahiminejad, A., & Vahidi, B. (2017). A novel physical based meta-heuristic optimization method known as Lightning Attachment Procedure Optimization. Applied Soft Computing, 59, 596-621.

Nematollahi, A. F., Rahiminejad, A., & Vahidi, B. (2020). A novel meta-heuristic optimization method based on golden ratio in nature. Soft Computing, 24(2), 1117-1151.

Parsa, S., & Neshat, E. (2022). Thermodynamic and statistical analysis on the effect of exhaust gas recirculation on waste heat recovery from homogeneous charge compression ignition engines. Journal of Thermal Analysis and Calorimetry, 147(11), 6349-6361.

Rao, R. V., & Keesari, H. S. (2020). A self-adaptive population Rao algorithm for optimization of selected bio-energy systems. Journal of Computational Design and Engineering, 8(1), 69-96.

Roushangar, K., & Shahnazi, S. (2019). Bed load prediction in gravel-bed rivers using wavelet kernel extreme learning machine and meta-heuristic methods. International Journal of Environmental Science and Technology, 16(12), 8197-8208.

Satapathy, S., & Naik, A. (2016). Social group optimization (SGO): a new population evolutionary optimization technique. Complex & Intelligent Systems, 2(3), 173-203.

Shareef, H., Ibrahim, A. A., & Mutlag, A. H. (2015). Lightning search algorithm. Applied Soft Computing, 36, 315-333.

Tejani, G. G., Savsani, V. J., Patel, V. K., & Mirjalili, S. (2018). An improved heat transfer search algorithm for unconstrained optimization problems. Journal of Computational Design and Engineering, 6(1), 13-32.

Yazdani, M., & Jolai, F. (2015). Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm. Journal of Computational Design and Engineering, 3(1), 24-36.

Yilmaz, N., Ileri, E., Atmanlı, A., Deniz Karaoglan, A., Okkan, U., & Sureyya Kocak, M. (2016). Predicting the engine performance and exhaust emissions of a diesel engine fueled with hazelnut oil methyl ester: the performance comparison of response surface methodology and LSSVM. Journal of Energy Resources Technology, 138(5), 052206.

Zhao, W., Wang, L., & Zhang, Z. (2019). A novel atom search optimization for dispersion coefficient estimation in groundwater. Future Generation Computer Systems, 91, 601-610.

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